E-commerce has gained momentum with the rapid development of technology, and nowadays, we are permanently connected, with constant access to information and a wide range of products. Not only does a desktop computer offer us this possibility, but the latest-generation tablets and mobile phones create a broad framework. This paper investigates Romanian consumers’ attitudes towards adopting mobile technology for commerce (m-commerce), taking into account its development in the last few years, especially among younger generations. The main objectives of the research are to identify the preference for m-commerce use among Generation Z, establish the ways and the devices used by Gen Z individuals to inform about the products and services and order them, and analyze the factors influencing the use of m-commerce applications. The research methodology consists of conducting an empirical analysis using a distributed survey among youngsters from Generation Z in Romania. We used descriptive statistics, such as the analysis of frequency and the mean of variables, artificial neural network analysis (ANN), and multivariate analysis of variance (MANOVA), to validate the hypotheses. The research results indicate a solid inclination for m-commerce among Generation Z. The results are helpful for companies that can shape their marketing strategies to boost their sales using m-commerce channels among the younger population. Full article

Recently, the demand for location-based services using mobile devices in indoor spaces without a global positioning system (GPS) has increased. However, to the best of our knowledge, solutions that are fully applicable to indoor positioning and navigation and ensure real-time mobility on mobile devices, such as global navigation satellite system (GNSS) solutions, cannot achieve remarkable researches in indoor circumstances. Indoor single-shot image positioning using smartphone cameras does not require a dedicated infrastructure and offers the advantages of low price and large potential markets owing to the popularization of smartphones. However, existing methods or systems based on smartphone cameras and image algorithms encounter various limitations when implemented in indoor environments. To address this, we designed an indoor visual positioning system for mobile devices that can locate users in indoor scenes. The proposed method uses a smartphone camera to detect objects through a single image in a web environment and calculates the location of the smartphone to find users in an indoor space. The system is inexpensive because it integrates deep learning and computer vision algorithms and does not require additional infrastructure. We present a novel method of detecting 3D model objects from single-shot RGB data, estimating the 6D pose and position of the camera and correcting errors based on voxels. To this end, the popular convolutional neural network (CNN) is improved by real-time pose estimation to handle the entire 6D pose estimate the location and direction of the camera. The estimated position of the camera is addressed to a voxel to determine a stable user position. Our VPS system provides the user with indoor information in 3D AR model. The voxel address optimization approach with camera 6D position estimation using RGB images in a mobile web environment outperforms real-time performance and accuracy compared to current state-of-the-art methods using RGB depth or point cloud. Full article

Magnetic fields have attracted considerable attention in indoor localization due to their ubiquitous and infrastructure-free characteristics. This survey provides a comprehensive review of magnetic-field-based indoor localization methods. We first introduce characteristics of the magnetic field, its advantages, and its challenges. We then describe the magnetometer model and the effect of ferromagnetic interference. We also present coordinate systems commonly used for magnetic field localization and describe their transformation relationships. We then compare the existing publicly available magnetic field benchmark datasets, present magnetometer calibration algorithms, and show how efficiently magnetic field maps can be built. We also summarize state-of-the-art magnetic field localization methods (e.g., magnetic landmarks, dynamic time warping, magnetic fingerprinting, filters, simultaneous localization and mapping, and neural network). The smartphone-based pedestrian dead reckoning approach is also reviewed. Full article

High-definition (HD) maps determine the location of the vehicle under limited visibility based on the location information of safety signs detected by sensors. If a safety sign disappears or changes, incorrect information may be obtained. Thus, map data must be updated daily to prevent accidents. This study proposes a map update system (MUS) framework that maps objects detected by a road map detection system and the object present in the HD map. Based on traffic safety signs notified by the Korean National Police Agency, 151 types of objects, including traffic signs, traffic lights, and road markings, were annotated manually and semi-automatically. Approximately 3,000,000 annotations were trained based on the you only look once (YOLO) model, suitable for real-time detection by grouping safety signs with similar properties. The object coordinates were then extracted from the mobile mapping system point cloud, and the detection location accuracy was verified by comparing and evaluating the center point of the object detected in the MUS. The performance of the groups with and without specified properties was compared and their effectiveness was verified based on the dataset configuration. A model trained with a Korean road traffic dataset on our testbed achieved a group model of 95% mAP and no group model of 70.9% mAP. Full article

Due to limited coverage, radio access provided by ground communication systems is not available everywhere on the Earth. It is necessary to develop a new three-dimensional network architecture in a bid to meet various connection requirements. Space–air–ground integrated networks (SAGINs) offer large coverage, but the communication quality of satellites is often compromised by weather conditions. To solve this problem, we propose an extended extreme learning machine (ELM) algorithm in this paper, which can predict the communication attenuation caused by rainy weather to satellite communication links, so as to avoid large path loss caused by bad weather conditions. Firstly, we use Internet of Things (IoT)-enabled sensors to collect weather-related data. Then, the system feeds the data to the extended ELM model to obtain a category prediction for blockage caused by weather. Finally, this information helps the selection of the data transmission link and thus improves the satellite routing performance. Full article

Traffic forecasting provides the foundational guidance for many typical applications in the smart city management, such as urban traffic control, congestion avoidance, and navigation guidance. Many researchers have focused on the spatio-temporal correlations under fixed topology structure in traffic network to improve the traffic forecasting accuracy. Despite their advantages, the existing approaches are not completely discussed that the association relationship among traffic network nodes are not invariable under different traffic conditions. In this paper, a novel traffic forecasting framework is proposed by integrating the dynamic association of traffic nodes with the spatio-temporal deep learning model. To be specific, an adaptive subgraph reformulation algorithm is designed first based on the specific forecasting interval to reduce the interference of irrelevant spatio-temporal information. After that, by enhancing the attention mechanism with the generative decoder, a spatio-temporal deep learning model with only one forward operation is proposed to avoid the degradation of accuracy in the long-term prediction, in which the spatio-temporal information and the external factors (such as weather and holiday) are fused together to be as an input vector. Based on the reformulated subgraph constructed of traffic nodes with closer spatio-temporal correlation, experiments show that the proposed framework consistently outperforms other GNN (Graph Neural Network)-based state-of-the-art baselines for various forecasting intervals on a real-world dataset. Full article

Process-induced variability is a growing concern in the design of analog circuits, and in particular for monolithic microwave integrated circuits (MMICs) targeting the 5G and 6G communication systems. The RF and microwave (MW) technologies developed for the deployment of these communication systems exploit devices whose dimension is now well below 100 nm, featuring an increasing variability due to the fabrication process tolerances and the inherent statistical behavior of matter at the nanoscale. In this scenario, variability analysis must be incorporated into circuit design and optimization, with ad hoc models retaining a direct link to the fabrication process and addressing typical MMIC nonlinear applications like power amplification and frequency mixing. This paper presents a flexible procedure to extract black-box models from accurate physics-based simulations, namely TCAD analysis of the active devices and EM simulations for the passive structures, incorporating the dependence on the most relevant fabrication process parameters. We discuss several approaches to extract these models and compare them to highlight their features, both in terms of accuracy and of ease of extraction. We detail how these models can be implemented into EDA tools typically used for RF and MMIC design, allowing for fast and accurate statistical and yield analysis. We demonstrate the proposed approaches extracting the black-box models for the building blocks of a power amplifier in a GaAs technology for X-band applications. Full article

Cyber-attacks are becoming progressively complicated; hence, the functional issues of intrusion-detection systems (IDSs) present ever-growing challenges. Failing to detect intrusions may jeopardize the trustworthiness of security services, such as privacy preservation, authenticity, and accessibility. To fight these risks, different organizations nowadays use a variety of approaches, techniques, and technologies to safeguard the systems’ credibility. Establishing policies and procedures, raising user awareness, implementing firewall and verification systems, controlling system access, and building computer-issue management groups are all examples of safeguarding methods. There is a lack of sufficient emphasis on the effectiveness of intrusion-detection systems. In enterprises, IDS is used to analyze the potentially dangerous activities taking place within the technological settings. The selection of efficient IDS is a challenging task for organizations. This research evaluates the impact of five popular IDSs for their efficiency and effectiveness in information security. The authors used the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS)-based integrated multi-criteria decision-making (MCDM) methodology to evaluate the efficacy of the popular IDSs. The findings of this research suggest that most of the IDSs appear to be highly potential tools. Even though Snort is extensively deployed, Suricata has a substantial advantage over Snort. Suricata uses multi-threading functionality in comparison to Snort to boost the processing performance. Full article

Although neural network quantization is an imperative technology for the computation and memory efficiency of embedded neural network accelerators, simple post-training quantization incurs unacceptable levels of accuracy degradation on some important models targeting embedded systems, such as MobileNets. While explicit quantization-aware training or re-training after quantization can often reclaim lost accuracy, this is not always possible or convenient. We present an alternative approach to compressing such difficult neural networks, using a novel variant of the ZFP lossy floating-point compression algorithm to compress both model weights and inter-layer activations and demonstrate that it can be efficiently implemented on an embedded FPGA platform. Our ZFP variant, which we call ZFPe, is designed for efficient implementation on embedded accelerators, such as FPGAs, requiring a fraction of chip resources per bandwidth compared to state-of-the-art lossy compression accelerators. ZFPe-compressing the MobileNet V2 model with an 8-bit budget per weight and activation results in significantly higher accuracy compared to 8-bit integer post-training quantization and shows no loss of accuracy, compared to an uncompressed model when given a 12-bit budget per floating-point value. To demonstrate the benefits of our approach, we implement an embedded neural network accelerator on a realistic embedded acceleration platform equipped with the low-power Lattice ECP5-85F FPGA and a 32 MB SDRAM chip. Each ZFPe module consumes less than 6% of LUTs while compressing or decompressing one value per cycle, requiring a fraction of the resources compared to state-of-the-art compression accelerators while completely removing the memory bottleneck of our accelerator. Full article

Biomarkers have already been proposed as powerful classification features for use in the training of neural network-based and other machine learning and artificial intelligence-based prognostic models in the scientific field of personalized nutrition. In this paper, we construct and study cascaded SVM-based classifiers for automated metabolic syndrome diagnosis. Specifically, using blood exams, we achieve an average accuracy of about 84% in correctly classifying body mass index. Similarly, cascaded SVM-based classifiers achieve a 74% accuracy in correctly classifying systolic blood pressure. Next, we propose and implement a system that achieves an 84% accuracy in metabolic syndrome prediction. The proposed system relies not only on prediction of the body mass index but also on prediction from blood exams of total cholesterol, triglycerides and glucose. For the aim of self-completeness of the paper, the key concepts with regard to metabolic syndrome are summarized, and a review of previous related work is included. Finally, conclusions are drawn and indications for related future research are outlined. Full article

In the context of the evolution of in-vehicle electronic and electrical architecture as well as the rapid development of quantum computers, post-quantum algorithms, such as NTRUEncrypt, are of great significance for in-vehicle secure communications. In this paper, we propose and evaluate, for the first time, a NTRUEncrypt enhanced session key negotiation for the in-vehicle Ethernet context. Specifically, the time consumption and memory occupation of the NTRUEncrypt Elliptic Curve Diffie–Hellman (ECDH), and Rivest–Shamir–Adleman (RSA) algorithms, which are used for session key negotiation, are measured and compared. The result shows that, besides the NTRUEncrypt’s particular attribute of resisting quantum computer attacks, the execution speed of session key negotiation using NTRUEncrypt is 66.06 times faster than ECDH, and 1530.98 times faster than RSA at the 128-bit security level. The memory occupation of the algorithms is at the same order of magnitude. As the transport layer security (TLS) protocol can fulfill most performance requirements of the automotive industry, post-quantum enhanced session key negotiation will probably be widely used for in-vehicle Ethernet communication. Full article

People getting involved with modern agriculture should become familiar with and able to exploit the plethora of cutting-edge technologies that have recently appeared in this area. The contribution of the educational robotics in demystifying new scientific fields for K-12 students is remarkable, but things become more challenging when trying to discover efficient practices for higher education. Indeed, there is an apparent need for pilot examples facilitating students’ professional skills acquisition and thus matching the potential of the actual systems used in the modern agricultural premises. In this regard, this work discuses laboratory experiences while implementing an automatic airflow control system of convincing size and role capable for remote configuration and monitoring. This non-conventional robotic example exploits retired electromechanical equipment, from an old farm, and revives it using modern widely available microcontrollers, smart phones/tablets, network transceivers, motor drivers, and some cheap and/or custom sensors. The contribution of the corresponding software parts to this transformation is of crucial importance for the success of the whole system. Thankfully, these parts are implemented using easy-to-use programming tools, of open and free nature at most, that are suitable for the pairing credit-card-sized computer systems. The proposed solution is exhibiting modularity and scalability and assists students and future professionals to better understand the role of key elements participating in the digital transformation of the agricultural sector. The whole approach has been evaluated from both technical and educational perspective and delivered interesting results that are also reported. Full article

In this paper, we propose a multistage transimpedance amplifier (TIA) based on the local negative feedback technique. Compared with the conventional global-feedback technique, the proposed TIA has the advantages of a wider bandwidth, and lower power dissipation. The schematic and characteristics of the proposed TIA circuit are described. Moreover, the proposed TIA employs inductive peaking to increase bandwidth. The TIA is implemented using a 65 nm complementary metal oxide semiconductor (CMOS) technology and consumes 23.9 mW with a supply voltage of 1.0 V. Using a back-annotated simulation, we obtained the following characteristics: a gain of 46 dB$\mathsf{\Omega }$ and −3 dB frequency of 11.4 GHz. TIA occupies an area of 366 $\mathsf{\mu }$m × 225 $\mathsf{\mu }$m. Full article

The coverage rate is the most crucial index in wireless sensor networks (WSNs) design; it involves making the sensors with a reasonable distribution, which closely relates to the quality of service (QoS) and survival period of the entire network. This article proposes to use particle swarm optimization (PSO) and chaos optimization in conjunction for the coverage optimization. All sensor locations are encoded together as a particle position. PSO was used first to make sensors move close to their optimal positions; furthermore, a variable domain chaos optimization algorithm (VDCOA) was employed to reach a higher coverage rate, along with improved evenness and average moving distance. Six versions of VDCOA, taking circle, logistic, Gaussian, Chebyshev, sinusoidal and cubic maps, respectively, were investigated. The simulation experiment tested three cases: square, rectangular and circular regions using nine algorithms: six versions of PSO plus VDCOA, PSO and other two PSO variants. All six versions showed better performance than PSO and CPSO, with coverage all exceeding 90% for the first two cases. Moreover, one version, PSO plus circle map (PSO-Circle), increased the coverage rate by 3.17%, 2.41% and 12.94% compared with PSO in three cases, respectively, and outperformed the other eight algorithms. Full article

When constructing a deep learning model for recognizing violence inside a vehicle, it is crucial to consider several aspects. One aspect is the computational limitations, and the other is the deep learning model architecture chosen. Nevertheless, to choose the best deep learning model, it is necessary to test and evaluate the model against adversarial attacks. This paper presented three different architecture models for violence recognition inside a vehicle. These model architectures were evaluated based on adversarial attacks and interpretability methods. An analysis of the model’s convergence was conducted, followed by adversarial robustness for each model and a sanity-check based on interpretability analysis. It compared a standard evaluation for training and testing data samples with the adversarial attacks techniques. These two levels of analysis are essential to verify model weakness and sensibility regarding the complete video and in a frame-by-frame way. Full article

In this letter, a postwall-slotline stepped impedance resonator (PWS-SIR) is proposed and applied to a bandpass filter (BPF) with a wide stopband. The proposed PWS-SIR-BPF comprises three U-shaped PWS-SIRs and two microstrip-slot feeding transitions. A PWS has a much lower impendence which a conventional slotline (CSL) cannot reach, so a much smaller impendence ratio of the PWS-SIR can be achieved. Consequently, a wider stopband simultaneously can be realized for the proposed filter. The designed PWS-SIR-BPF, as well as a CSL-BPF, have been fabricated, measured, and compared to verify the features of the PWS-SIR. The measured results are consistent with the simulation ones. The PWS-SIR is 7.3 mm (0.22λ₀) long, 67% of 11.1 mm (0.34λ₀) of the CSL resonator. The first spurious resonance frequency of the PWS-SIR-BPF is extended from 9.8 GHz (2f₀) to 23 GHz (4.7f₀). Full article

A full smart grid implementation requires the digitization of all parts of the smart grid infrastructure, including secondary electrical substations. Unfortunately, this introduces new security threats, which were not apparent before. This article uses a Smart Grid Threat Modeling Template implementing the STRIDE model to create a threat model of a digital secondary substation and its communication with the control center. Threats are classified by priority and need for further investigation. The tool was compared with a CORAS analysis, and was determined to be more time efficient. Denial of service (DoS) threats were classified as the most critical, and they were further evaluated in a precise simulation model created for this purpose. This model combines simulation with emulated communication, and enables verification of threat likelihoods and impacts. The results show that even publicly available tools can be easily used to disrupt grid communication and potentially cause loss of the entire grid’s observability and controllability. Full article

Die-level thinning, handling, and integration of singulated dies from multi-project wafers (MPW) are often used in research, early-stage development, and prototyping of flexible devices. There is a high demand for thin silicon devices for several applications, such as flexible electronics. To address this demand, we study a novel post-processing method on two silicon devices, an electrochemical impedance sensor, and Complementary Metal Oxide Semiconductor (CMOS) die. Both are drawn from an MPW batch, thinned at die-level after dicing and singulation down to 60 µm. The thinned dies were flip-chip bonded to flexible substrates and hermetically sealed by two techniques: thermosonic bonding of Au stud bumps and anisotropic conductive paste (ACP) bonding. The performance of the thinned dies was assessed via functional tests and compared to the original dies. Furthermore, the long-term reliability of the flip-chip bonded thinned sensors was demonstrated to be higher than the conventional wire-bonded sensors. Full article

Wireless network devices are currently a hot topic in research related to human health, control systems, smart homes, and the Internet of Things (IoT). In the shadow of the coronavirus pandemic, they have gained even more attention. This remote and contactless distributed sensing technology enabled monitoring of vital signs in real-time. Many of the devices are battery powered, so appropriate management of available energy is crucial for lengthening autonomous operation time without affecting weight, size, maintenance requirement, and user acceptance. In this paper, we discuss energy consumption aspects of sensor data transmission using wireless Bluetooth Low Energy Mesh Long Range (BLE-M-LR) technology. Papers in the field of energy savings in wireless networks do not directly address the problem of the dependence of the energy needed for transmission on the type and degree of data preprocessing, which is the novelty and uniqueness of this work. We built and studied a prototype system designed to work as a multimodal sensing node in a compound IoT application targeted to assisted living. To analyze multiple energy-related aspects, we tested it in various operation and data transmission modes: continuous, periodic, and event-based. We also implemented and tested two alternative sensor-side processing procedures: deterministic data stream reduction and neural network-based recognition and labeling of the states. Our results reveal that event-based or periodic operation allows the node for years-long operating, and the sensor-side processing may degrade the power economy more than it benefits from savings made on transmission of concise data. Full article

Each sparse representation classifier has different classification accuracy for different samples. It is difficult to achieve good performance with a single feature classification model. In order to balance the large-scale information and global features of images, a robust dictionary learning method based on image multi-vector representation is proposed in this paper. First, this proposed method generates a reasonable virtual image for the original image and obtains the multi-vector representation of all images. Second, the same dictionary learning algorithm is used for each vector representation to obtain multiple sets of image features. The proposed multi-vector representation can provide a good global understanding of the whole image contour and increase the content of dictionary learning. Last, the weighted fusion algorithm is used to classify the test samples. The introduction of influencing factors and the automatic adjustment of the weights of each classifier in the final decision results have a significant indigenous effect on better extracting image features. The study conducted experiments on the proposed algorithm on a number of widely used image databases. A large number of experimental results show that it effectively improves the accuracy of image classification. At the same time, to fully dig and exploit possible representation diversity might be a better way to lead to potential various appearances and high classification accuracy concerning the image. Full article

Sentiment analysis has been one of the most active research areas in the past decade due to its vast applications. Sentiment quantification, a new research problem in this field, extends sentiment analysis from individual documents to an aggregated collection of documents. Sentiment analysis has been widely researched, but sentiment quantification has drawn less attention despite offering a greater potential to enhance current business intelligence systems. In this research, to perform sentiment quantification, a framework based on feature engineering is proposed to exploit diverse feature sets such as sentiment, content, and part of speech, as well as deep features including word2vec and GloVe. Different machine learning algorithms, including conventional, ensemble learners, and deep learning approaches, have been investigated on standard datasets of SemEval2016, SemEval2017, STS-Gold, and Sanders. The empirical-based results reveal the effectiveness of the proposed feature sets in the process of sentiment quantification when applied to machine learning algorithms. The results also reveal that the ensemble-based algorithm AdaBoost outperforms other conventional machine learning algorithms using a combination of proposed feature sets. The deep learning algorithm RNN, on the other hand, shows optimal results using word embedding-based features. This research has the potential to help diverse applications of sentiment quantification, including polling, trend analysis, automatic summarization, and rumor or fake news detection. Full article

Multiport magnetoinductive (MI) devices with directional filter properties are presented. Design equations are developed and solved using wave analysis and dispersion theory, and it is shown that high-performance directional filters can be realised for use both in MI systems with complex, frequency-dependent impedance and in conventional systems with real impedance. Wave analysis is used to reduce the complexity of circuit equations. High-performance MI structures combining directional and infinite rejection filtering are demonstrated, as well as multiple-passband high-rejection filtering. A new method for improving filtering performance through multipath loss compensation is described. Methods for constructing tuneable devices using toroidal ferrite-cored transformers are proposed and demonstrated, and experimental results for tuneable MI directional filters are shown to agree with theoretical models. Limitations are explored, and power handling sufficient for HF RFID applications is demonstrated, despite the use of ferrite materials. Full article

Wireless-powered communication networks (WPCNs) consist of wireless devices (WDs) that transmit information to the hybrid access point (HAP). In this situation, there is interference among WDs that is considered to be noise and causes information loss because of adjacent signals. Moreover, power is limited and can be lost if transmission distance is long. This paper studies sum-throughput maximization with sectored cells for WPCN. We designed a downlink (DL) energy beamforming by sector based on the hybrid space division multiple access (SDMA) and nonorthogonal multiple access (NOMA) approach to maximize the sum throughput. First, a cell is divided into several sectors, and signals from each sector are transmitted to each antenna of the HAP, so that the signals are not adjacent. Further, the HAP decodes the overlapping information of each sector. Next, power allocation is optimized by sector. To optimize power allocation, a constrained optimization problem is formulated and then converted into a nonconstraint optimization problem using the interior penalty method. The optimal solution derives the maximal value to the problem. Power for each sector is optimally allocated according to this optimal solution. Under this consideration, sum-throughput maximization is performed by optimally allocating DL energy beamforming by sector. We analyzed sum throughput and fairness, and then compared them according to the number of sectors. Performance results show that the proposed optimal power allocation by sector using hybrid SDMA/NOMA outperforms the existing equal power allocation by sector in terms of the sum throughput while fairness is also maintained. Moreover, the performance difference between the hybrid approach and SDMA, which optimally allocates power by sector, was about 1.4 times that of sum throughput on average, and the hybrid approach was dominant. There was also no difference in fairness performance. Full article

Edge computing (processing data close to its source) is one of the fastest developing areas of modern electronics and hardware information technology. This paper presents the implementation process of an analog CMOS preprocessor for use in a distributed environment for processing medical data close to the source. The task of the circuit is to analyze signals of vesicle fusion, which is the basis of life processes in multicellular organisms. The functionality of the preprocessor is based on a classifier of full and partial fusions. The preprocessor is dedicated to operate in amperometric systems, and the analyzed signals are data from carbon nanotube electrodes. The accuracy of the classifier is at the level of 93.67%. The implementation was performed in the 65 nm CMOS technology with a 0.3 V power supply. The circuit operates in the weak-inversion mode and is dedicated to be powered by thermal cells of the human energy harvesting class. The maximum power consumption of the circuit equals 416 nW, which makes it possible to use it as an implantable chip. The results can be used, among others, in the diagnosis of precancerous conditions. Full article

To deal with the problem of stable walking of quadruped robots on slopes, a gait planning algorithm framework for quadruped robots facing unknown slopes is proposed. We estimated the terrain slope by the attitude information measured by the inertial measurement unit (IMU) without relying on the robot vision. The crawl gait was adopted, and the center of gravity trajectory planning was carried out based on the stability criterion zero-moment point (ZMP). Then, the augmented random search (ARS) algorithm was used to modulate the parameters of the Bezier curve to realize the planning of the robot foot trajectory. Additionally, the robot can adjust the posture in real time to follow the desired joint angle, which realizes the adaptive adjustment of the robot’s posture during the slope movement. Simulation experiment results show that the proposed algorithm for slope gait planning can adaptively adjust the robot’s attitude and stably pass through the slope environment when the slope is unknown. Full article

This paper traces the history of the evolution of inverse analog filters (IAF) and presents a review of the progress made in this area to date. The paper, thus, presents the current state-of-the art of IAFs by providing an appraisal of a variety of realizations of IAFs using commercially available active building blocks (ABB), such as operational amplifiers (Op-amp), operational transconductance amplifiers (OTA), current conveyors (CC) and current feedback operational amplifiers (CFOA) as well as those realized with newer active building blocks of more recent origin, such as operational transresistance amplifiers (OTRA), current differencing buffered amplifiers (CDBA) and variants of current conveyors which, although not available as off-the-shelf ICs yet, can be implemented as complementary metal–oxide–semiconductors (CMOS) or be realized in discrete form using other commercially available integrated circuits (IC). In the end, some issues related to IAFs have been highlighted which need further investigation. Full article

A millimeter-wave direct-conversion radio-frequency (RF) transmitter requires precise in-/quadrature-phase (I/Q) mismatch calibration and dc offset cancellation to minimize image rejection ratio (IRR) and LO feedthrough (LOFT) for ensuring satisfactory output spectral purity. We present a 28-GHz CMOS RF transmitter with an improved calibration technique for fifth generation (5G) wireless communication applications. The RF transmitter comprises a baseband amplifier, quadrature up-conversion mixer, power amplifier driver, and quadrature LO generator. The I/Q amplitude mismatch is calibrated by tuning the gate biases of the switching stage FETs of the mixer, the I/Q phase mismatch is calibrated by tuning the varactor capacitances at the LC load of LO buffer, and the dc offset is cancelled by tuning the body voltages of the differential-pair FETs at the baseband amplifier. The proposed technique provides precise calibration accuracy by employing mV-resolution tuning voltage generation via 6-bit voltage digital-to-analog converters. It also covers wide calibration range while minimizing the impact on the circuit’s bias point and dissipated current during calibration. Implemented in a 65 nm CMOS process, the RF transmitter integrated circuit shows output-referred 1 dB compression power of +6.5 dBm, saturated output power of +12.6 dBm, and an operating band of 27.5–29.3 GHz while demonstrating satisfactory performances of −55.9 dBc of IRR and −36.8 dBc of LOFT. Full article

With the development of the smart grid, massive electric Internet of Things (EIoT) devices are deployed to collect data and offload them to edge servers for processing. However, the task of offloading optimization still faces several challenges, such as the differentiated quality of service (QoS) requirements, decision coupling among multiple devices, and the impact of electromagnetic interference. In this paper, a low-complexity delay and energy-efficiency-balanced task offloading algorithm based on many-to-one two-sided matching is proposed for an EIoT. The proposed algorithm shows its novelty in the dynamic tradeoff between energy efficiency and delay as well as in low-complexity and stable task offloading. Specifically, we firstly formulate the minimization problem of weighted difference between delay and energy efficiency to realize the joint optimization of differentiated QoS requirements. Then, the joint optimization problem is transformed into a many-to-one two-sided matching problem. Through continuous iteration, a stable matching between devices and servers is established to cope with decision coupling among multiple devices. Finally, the effectiveness of the proposed algorithm is validated through simulations. Compared with two advanced algorithms, the weighted difference between the energy efficiency and delay of the proposed algorithm is increased by 29.01% and 45.65% when the number of devices is 120, and is increased by 11.57% and 22.25% when the number of gateways is 16. Full article

A narrow bandgap of a few layers of platinic disulfide (PtS₂) has shown great advantages in large-area array photodetectors for wide spectra photodetection, which is necessary for infrared imaging and infrared sensing under extreme conditions. The photodetection performance of two dimensional materials is highly dependent on the crystalline quality of the film, especially under high operating temperatures. Herein, we developed large area uniform array photodetectors using a chemical vapor deposition grown on PtS₂ films for short-wave infrared photodetection at high operating temperature. Due to the high uniformity and crystalline quality of as-grown large area PtS₂ films, as-fabricated PtS₂ field effect transistors have shown a broadband photo-response from 532 to 2200 nm with a wide working temperature from room temperature to 373 K. The photo-responsivity (R) and specific detectivity (D*) of room temperature and 373 K are about 3.20 A/W and 1.24 × 10⁷ Jones, and 839 mA/W and 6.1 × 10⁶ Jones, at 1550 nm, respectively. Our studies pave the way to create an effective strategy for fabricating large-area short-wave infrared (SWIR) array photodetectors with high operating temperatures using chemical vapor deposition (CVD) grown PtS₂ films. Full article

The creation of synthetic data are important for a range of applications, for example, to anonymise sensitive datasets or to increase the volume of data in a dataset. When the target dataset has missing data, then it is common to just discard incomplete observations, even though this necessarily means some loss of information. However, when the proportion of missing data are large, discarding incomplete observations may not leave enough data to accurately estimate their joint distribution. Thus, there is a need for data synthesis methods capable of using datasets with missing data, to improve accuracy and, in more extreme cases, to make data synthesis possible. To achieve this, we propose a novel generative adversarial network (GAN) called MaWGAN (for masked Wasserstein GAN), which creates synthetic data directly from datasets with missing values. As with existing GAN approaches, the MaWGAN synthetic data generator generates samples from the full joint distribution. We introduce a novel methodology for comparing the generator output with the original data that does not require us to discard incomplete observations, based on a modification of the Wasserstein distance and easily implemented using masks generated from the pattern of missing data in the original dataset. Numerical experiments are used to demonstrate the superior performance of MaWGAN compared to (a) discarding incomplete observations before using a GAN, and (b) imputing missing values (using the GAIN algorithm) before using a GAN. Full article

Faults in distribution overhead lines occur due to various reasons, such as rain, strong winds, lightning, and other natural causes. The protection from the load side broken conductor (LSBC) faults has been one of the biggest challenges in the power distribution network. The small current generated by the LSBC fault makes the traditional protection system unable to detect this type of fault. The danger of LSBC fault is still enormous; besides, the available works of literature addressing this issue face difficulties when applying it to the real power system. This paper proposes a new method for detecting LSBC fault using single-ended measurements to the overhead distribution lines. The detection method is based on the constant ratio of negative to positive sequence current measured at the feeder end. The proposed study is performed using MATLAB software to implement a real network as a case study and verified by mathematical analysis. According to obtained results, we demonstrated that the fault in the electrical network had been detected with 100% of feeder protection. The proposed method has the benefit of being applicable and compatible with the existing measurement equipment, even when used in conjunction with overcurrent and earth fault relay in the electrical substation. Therefore, the negative to positive sequence currents are powerful in aiding fault detection. The benefit of this approach is providing a suitable LSBC protection solution for utilities while also opening new prospects in fault detection techniques in the distribution system. Full article

Crowd monitoring was an essential measure to deal with over-tourism problems in urban destinations in the pre-COVID era. It will play a crucial role in the pandemic scenario when restarting tourism and making destinations safer. Notably, a Destination Management Organisation (DMO) of a smart destination needs to deploy a technological layer for crowd monitoring that allows data gathering in order to count visitors and distinguish them from residents. The correct identification of visitors versus residents by a DMO, while privacy rights (e.g., Regulation EU 2016/679, also known as GDPR) are ensured, is an ongoing problem that has not been fully solved. In this paper, we describe a novel approach to gathering crowd data by processing (i) massive scanning of WiFi access points of the smart destination to find SSIDs (Service Set Identifier), as well as (ii) the exposed Preferred Network List (PNL) containing the SSIDs of WiFi access points to which WiFi-enabled mobile devices are likely to connect. These data enable us to provide the number of visitors and residents of a crowd at a given point of interest of a tourism destination. A pilot study has been conducted in the city of Alcoi (Spain), comparing data from our approach with data provided by manually filled surveys from the Alcoi Tourist Info office, with an average accuracy of 83%, thus showing the feasibility of our policy to enrich the information system of a smart destination. Full article